One of the underlying premises of ecosystem management (Samson and Knopf 1996, Boyce
and Haney 1997, Kohm and Franklin 1997, Vogt et al. 1997) is that ecosystems and landscapes
are dynamic; that disturbance (both natural and anthropogenic) and succession processes create
patterns in the distribution of resources that feed back to affect these and other ecological
processes and maintain ecosystems and landscapes in a constant state of flux (Turner 1989a).
Adopting this ‘dynamic view' and incorporating it into land management strategies has become a
major challenge for land management agencies (Franklin 1997). Recently, in fact, it was
proposed as a mandate for the U.S. Forest Service (Federal Register, "National Forest System
Land and Resource Management Planning. Final Rule 36 CFR Parts 217 and 219" Federal
Register 65: 65714-65781). Although this ‘dynamic view' has become the operating paradigm in
landscape ecology and land management (Turner et al. 2001), it is largely understood in concept
only; a comprehensive quantitative framework still eludes scientists and managers, primarily due
to a lack of quantitative studies (both empirical and theoretical).

Landscape ecology is based on the premise that landscape patterns influence ecosystem
processes over a wide range of spatial and temporal scales (Urban et al. 1987, Turner 1989, 1990,
Forman 1995, Turner et al. 2001). Processes such as disturbance and succession play a central
role in structuring ecological systems by producing a spatiotemporal mosaic of vegetation
patches in different successional states (Paine and Levin 1981, Sousa 1984, Krummel et al 1987).
This shifting mosaic of vegetation patches has important implications for landscape function,
including the ability to support viable populations of plant and animal species (Forman 1995).
The distribution of species within this shifting mosaic depends upon an interaction between
species' life history traits and the spatial and temporal structure of the environment. Species with
different life history traits and habitat associations may respond very differently to landscape
changes (e.g., Hansen and Urban 1992). Landscape changes may cause some populations to
decline or be subdivided into disjunct populations due to direct loss of suitable habitat and/or
loss of connectivity among habitat patches, as in metapopulations (Levin and Paine 1974, Gilpin
and Hanski 1991, Weins 1997) and source-sink populations (Pulliam 1988). Conversely, the
same landscape changes may cause some populations to increase in size and/or viability due to
an increase in the amount and connectivity of suitable habitats. These varied responses to
landscape change make it exceedingly difficult for land managers to understand the ecological
consequences of proposed management actions.

Our study was motivated by the need to provide a better quantitative understanding of
landscape dynamics and was stimulated by four basic information needs.

First, the concept of natural variability has emerged as a paradigm for ecosystem
management, species conservation, and landscape restoration in western North America (Cissel
et al. 1994, Morgan et al 1994, Swanson et al. 1994), and it is increasingly believed that
maintaining landscapes within their "natural" range of variability is an effective “coarse-filter”
approach to maintaining ecological integrity (Landres et al 1999, Romme et al. 2000, Buse and
Perara 2002). This approach is based on the premise that ecosystems are naturally dynamic and
that native species have adapted to disturbance-driven fluctuations in their habitats (Bunnell
1995). Therefore, the potential for survival of any given species may diminish if temporal and
spatial patterns of species’ habitats shift outside their natural range of variation (Hessburg et al.
1999). An estimated range of natural variability can provide a frame of reference for assessing
landscape patterns and processes over an extended period of time (Swetnam et al. 1999,
Wimberley et al. 1999). One advantage of this approach is that it provides a basis for
understanding ecological systems and the changes occurring in these systems, as well as for
evaluating the long-term consequences of proposed management actions (Landres et al. 1999,
Wimberley et al. 1999). Although the notion of "natural" in ecological systems is equivocal
(Sprugel 1991), it is common to define an historical reference period during which human
activities had relatively minor effects on overall landscape structure and function, and use this as
a benchmark for comparison with contemporary or potential future conditions (Landres et al
1999, Romme et al. 2000, Buse and Perara 2002). A prerequisite to this management strategy is
knowledge of the range of variation in key landscape attributes during the reference period,
hereafter referred to as the historic range of variability (HRV). Moreover, a quantitative
understanding of HRV is essential if we are to know whether recent human activities have caused
landscapes to move outside their HRV (Landres et al.1999; Swetnam et al. 1999). See Romme et
al. (2003) for a detailed discussion of the concept of reference conditions.

Second, the notion of quantifying landscape dynamics immediately invokes issues of scale.
Indeed, it is well known that observed ecological patterns depend largely upon the scale that is
chosen for observation (Wiens 1989). Thus, it seems intuitively obvious that in a given landscape
subject to a specific disturbance regime (i.e., the spatial and temporal distribution of
disturbances), the measured range of variability will depend on the size of the landscape relative
to the size of the disturbances and the length of the time period over which landscape behavior is
examined. Turner and colleagues addressed this issue theoretically using a simple landscape
simulation model to address the question of how we might expect systems to behave over time,
given a specific disturbance regime and a particular reference area (e.g., study area) (Turner et al.
1993). They noted three things: (1) that the characteristic dynamics can be predicted from the
relative scaling of the disturbance regime, (2) that disturbance-driven landscapes might be
equilibrium, quasi-equilibrium, or inherently nonequilibrium; and (3) that anthropogenic
influences may rescale these and change the qualitative dynamics of systems (e.g., fire
suppression rescales a fire regime). Despite the clarity of this theoretical framework, few studies
have explored this relationship empirically (e.g., Wimberly et al. 2000, McGarigal et al. 2001).

Third, myriad evidence exists that landscapes are naturally dynamic, fluctuating in structure
and function over time in response to the interplay of disturbance and succession processes (e.g.,
Romme 1982, Romme and Despain 1989, Baker 1992, Wallin et al. 1994 and 1996), yet there is
a paucity of evidence that dynamism itself is essential to the maintenance of ecological integrity.
It is well known that landscapes and the populations they contain are dynamic over time (Forman
1995, Turner et al. 2001). Yet, many landscapes contain areas that continually escape some
disturbance processes, while other areas are disturbed at a much higher rate. Such a wide range of
temporal dynamics may have profound effects on population persistence (Fahrig 1992, Reeves et
al. 1995). For example, during times of low habitat availability, areas that continually escape
disturbance may function as refugia. When habitat area increases, these refugia may function as
source areas for population expansion. Therefore, certain populations may be as dependent on the
disturbance patterns determining the temporal structure of habitats as they are on the spatial
distribution of habitats. Unfortunately, while many studies have addressed the spatial structure of
habitats (Kareiva and Wennergren 1995, Tilman and Kareiva 1997), few studies have addressed
the temporal structure of habitats (Fahrig 1992, Johnson 2000, Keymer et al. 2000) or the
importance of these temporal dynamics to population persistence (Fahrig 1992, Reeves et al.
1995).

Fourth, only by understanding and quantifying the long-term dynamics of landscapes can we
provide the proper perspective for interpreting current and future landscape patterns and make
informed land use decisions consistent with the goals of ecosystem management (Gustafson
1998, McGarigal 2002). Due in part to widely available software programs like FRAGSTATS
(McGarigal and Marks 1995, McGarigal et al. 2002), it is now quite easy to compute a wide
variety of landscape metrics for categorical maps. This has helped advance the field of
quantitative landscape ecology and has provided land managers with a means of quantitatively
evaluating the impacts of alternative management scenarios on landscape structure.
Unfortunately, quantifying landscape structure is fraught with many difficulties which are often
overlooked or not given serious consideration. Perhaps the single greatest difficulty in
interpreting landscape metrics is the lack of a good reference framework. Often we wish to
interpret the meaning of a landscape metric computed for the current condition of a landscape,
yet that interpretation is inextricably linked to the historic pattern of variability in that metric.
Developing a quantitative understanding of the metric behavior under historic reference period
conditions is prerequisite to an informed ecological interpretation of the metric and of the current
and future state of the landscape.